A process which has received wide acceptance in the steel industry for refining metal is the argon-oxygen decarburization process also referred to as the "AOD" process. It is the purpose of AOD refining to first remove carbon from a bath of metal, next reduce any metals that may have oxidized during decarburization, and finally adjust the temperature and chemistry of the bath before casting the metal into a product. Decarburization is achieved by injecting mixtures of oxygen and inert gases in such a way as to favor the oxidation of carbon over the oxidation of other metal components present in the bath. At progressively lower carbon contents during the process of decarburization progressively greater dilution of the oxygen by inert gases is injected to favor the oxidation or removal of carbon.
Relationships between the bath weight, chemistry, and temperature, the injections of oxygen and inert gases, and the resultant changes in metal chemistry and temperature have been theorized to achieve both control and understanding of how to optimize the economics of the process. Thermodynamic models have tracked the general relationships between these parameters, but have limited accuracy and have not obviated the need for intermediate sampling of the bath temperature and chemistry in processing any given heat of metal. Some theorists have adopted the approach that the decarburization reaction may be better understood, and hence controlled, by considering the chemical kinetics of the competing oxidations of carbon and the various metal species present. It follows that approaches incorporating both thermodynamic and kinetic considerations have also been constructed. Finally, statistical approaches have been used to empirically model decarburization in an AOD converter.
The traditional modeling of the decarburization cycle of the AOD operation requires not only a comprehensive understanding of how to represent the thermodynamics and/or kinetics for use in a computer program, but also requires the knowledge of many properties of the species involved in the reactions. For instance, normal thermodynamic modeling requires the knowledge of at least 25 pertinent interaction coefficients. The free enthalpies and entropies associated with each potential reaction must also be known as well as a representative pressure exerted on the bubbles passing through and reacting with the bath. Kinetic models that are based on assumptions that diffusion, adsorption and desorption rates significantly affect the relative extents to which the competing oxidation reactions occur are similarly dependent on accurate knowledge of these rates with respect to temperature and base composition. They must also be capable of modeling the surface areas, velocities of the bubbles relative to the surrounding liquid, and the residence times of the bubbles in the metal phase. Thus, the modeling of decarburization based on chemical theories is subject to many items of data being all accurately measured. They also require a correct understanding of the mechanisms of the various reactions. Since models are deficient in at least one of these two requirements, it is normal for known physical "constants" to be altered to make the results of the model fit actual results better. Due to the complexity of these models, great skill is required to adjust the parameters to improve the overall accuracy of an entire population of results. Often it is found that one particular solution or combination of adjusted constants is optimal for representing the results of only one particular set of working conditions. That is, solutions tend not to be general, but rather geared to specific small sets of data for which they were adjusted.
In spite of the variety of approaches, inaccuracies remain and some form of measuring the carbon content during the decarburization process step is normally required. This usually necessitates halting the process, withdrawing a metal sample, analyzing the carbon content and measuring the bath temperature before resuming. Lack of process control during decarburization not only necessitates extra sampling, but precludes operation at the optimal conditions for cost reduction and production maximization.
A computerized system using "neural networks" benefits from the fact that a theoretical understanding of decarburization is not required. Knowledge of the physical properties of the species and thermodynamic and kinetic reactions involved is also not required nor are the heat transfer properties of the reactor vessel required. Given the pertinent input parameters, a neural network can evaluate the input data and provide appropriate output data for controlling the decarburization operation based upon the recognition of patterns between the input and output data which it has learned through a learning or training procedure involving the evaluation of random examples presented to the neural network thousands of times.
The processing of a computer to perform parallel distributive processing logic based upon neural models which simulate the operation of the human brain is, in general, referred to as "neural networks". A neural network utilizes numerous nonlinear elements referred to as "neurons" to simulate the function of neurons in a human brain with each neuron representing a processing element. Each processing element is connected to other processing elements through a connecting weight or "synapse" which is combined by summation. The connecting weights are modified by adaptive learning from multiple examples. Once trained, the neural network is capable of recognizing a pattern between the input and output data which may be utilized, as hereinafter explained in detail, to provide information for controlling a decarburization operation without concern for the thermodynamic activity of the constituents in the bath and/or the kinetics of the reactions. The bath represents the mass of molten metal which is transferred to a refractory lined vessel to be refined in accordance with the present invention.